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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL 12, NO 3, SEPTEMBER 2011 659 Energy-Efficient Cooperative Techniques for Infrastructure-to-Vehicle Communications Tuan-Duc Nguyen, Olivier Berder, and Olivier Sentieys, Member, IEEE Abstract—In wireless distributed networks, cooperative relay and cooperative multiple-input–multiple-output (MIMO) techniques can be used to exploit the spatial and temporal diversity gains to increase the performance or reduce the transmission energy consumption The energy efficiency of cooperative MIMO and relay techniques is then very useful for the infrastructureto-vehicle (I2V) and infrastructure-to-infrastructure (I2I) communications in intelligent transport system (ITS) networks, where the energy consumption of wireless nodes embedded on road infrastructure is constrained In this paper, applications of cooperation between nodes to ITS networks are proposed, and the performance and the energy consumption of cooperative relay and cooperative MIMO are investigated and compared with the traditional multihop technique The comparison between these cooperative techniques helps us choose the optimal cooperative strategy in terms of energy consumption for energy-constrained road infrastructure networks in ITS applications Index Terms—Cooperative multiple-input–multiple-output (MIMO), distributed space-time coding, energy efficiency, infrastructure-to-vehicle communications, wireless communications I I NTRODUCTION I N future intelligent transport systems (ITS), information and communication from the road infrastructure to vehicle (I2V) will play a key role in driving assistance, floating car data, and traffic management to make the road safer and more intelligent The communications are supported by wireless nodes that are integrated in road signs (or traffic infrastructure along the road) and vehicles Although wireless nodes that are embedded in vehicles can take profit from their battery or can regularly be recharged, each road sign wireless node is usually powered by a small battery that may not be rechargeable or renewable for a long time (or powered by a low power solar battery) Even if such networks are mainly concentrated in cities (but new applications also appear for rural junctions), many of the nodes are not necessarily connected to an electrical power supply due to the civil engineering cost The energy consumption of road infrastructure wireless nodes is, consequently, one of the Manuscript received August 15, 2009; revised September 13, 2010; accepted February 7, 2011 Date of publication March 17, 2011; date of current version September 6, 2011 The Associate Editor for this paper was S Ukkusuri T.-D Nguyen is with the School of Electrical Engineering, Ho Chi Minh City International University, Vietnam National University, Ho Chi Minh City 70000, Vietnam (e-mail: ntduc@hcmiu.edu.vn) O Berder and O Sentieys are with the Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), University of Rennes 1, 35042 Rennes Cedex, France (e-mail: oberder@irisa.fr; sentieys@irisa.fr) Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org Digital Object Identifier 10.1109/TITS.2011.2118754 important constraints when increasing the reliability and the lifetime of this network As the transmission power quickly increases as a K power function of the transmission distance (with typical path loss factor < K < 6), the transmission energy consumption plays an important role for medium- and long-range transmission and represents the dominant part of the total energy consumption In some ITS applications, energy-efficient transmission techniques are very important for the communication from an energy-constrained device such as road I2V or to another energy-constrained device [road infrastructure to road infrastructure (I2I)] In the traditional approach, the multihop transmission technique is used to reduce the transmission energy consumption by dividing the long transmission channel into multiple short transmissions The cooperative relay technique can exploit the spatial and temporal diversity gains to reduce the path loss effect in wireless channels The result is that the system performance is improved or less energy is needed for data transmission Relay techniques are recognized as a simple energy-efficient way of extending the transmission range due to their simplicity and their performance for wireless transmissions over fading channels [1]–[3] These techniques have recently been studied in the context of vehicle-to-vehicle (V2V) communications in [4] Aside from the relay technique, some individual sensor nodes can cooperate at the transmission and the reception to deploy a cooperative multiple-input–multiple-output (MIMO) transmission scheme [5]–[7] Classical MIMO transmission is investigated for V2V transmissions and should be proposed in the future IEEE 802.11.p standard Unfortunately, nodes that are embedded in the road signs cannot have more than one antenna because of the limitations in space, cost, and energy consumption Therefore, classical MIMO cannot be applied to I2I and I2V communications On the other hand, cooperative MIMO can exploit the diversity gain of the space–time coding technique to increase the system performance or to reduce the energy consumption In [8] and [9], it has been shown that cooperative multiple-input–single-output (MISO) and MIMO systems are more energy efficient than single-input–singleoutput (SISO) and traditional multihop SISO systems for medium- and long-range transmission in wireless distributed sensor networks Other recent works on MIMO space–time block code (STBC) transmission in ITS applications can be found in [10] and [11] One the other hand, cooperation between nodes can also help extend the transmission range (with the same output power of one wireless node), thus increasing the communication distance between two nodes or two groups of nodes 1524-9050/$26.00 © 2011 IEEE 660 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL 12, NO 3, SEPTEMBER 2011 Fig I2I and I2V wireless communications in the CAPTIV Project In this paper, these cooperative techniques are adopted to ITS applications and characterized for I2V and I2I cooperative transmissions The context of this paper is the Cooperative Strategies for Low-Power Wireless Transmissions Between Infrastructure and Vehicles (CAPTIV) Project [12], where a network composed of wireless nodes at a junction has to give arriving vehicles short-term information for driving assistance and long-term information for traffic management It is shown that the cooperative MIMO and relay techniques are better than the SISO and multihop SISO techniques in terms of performance and energy consumption Both techniques are interesting in the energy-constrained ITS applications, and the advantages of each technique depend on the particular network structure or on the application Based on a reference model, energy consumption calculations help us choose the optimal cooperative strategy in terms of energy consumption for CAPTIV with respect to the transmission distances between two junctions or between a junction and a vehicle The rest of this paper is organized as follows The principle of cooperative strategies for the energy consumption optimization are presented in Section II In Section III, the energy calculation model is proposed, and simulation results on the energy consumption comparison of cooperative techniques in CAPTIV are presented Finally, conclusions and discussions are contained in Section IV Fig Three-terminal relay diversity scheme road) and arriving vehicle indications (e.g., to help a driver at a stop whether to start on the main road in case of smog, heavy rain, or snow) In such a network, every kind of information can be transmitted, leading to more advanced applications that integrate live data and feedback from a number of other sources, e.g., parking guidance and information systems, and weather information In the CAPTIV system, information is transmitted due to vehicles and existing infrastructure within a network whose typical size is metropolitan The communications can occur from I2V, I2I, a vehicle to road infrastructure (V2I), or from one vehicle to another vehicle (V2V) The energy constraint for road sign infrastructure is very important, because batteries in traffic road signs cannot be replaced for a long time A Relay and Cooperative MIMO Techniques II C OOPERATIVE T RANSMISSIONS AND C OOPERATIVE S TRATEGIES FOR L OW-P OWER W IRELESS T RANSMISSIONS B ETWEEN I NFRASTRUCTURE AND V EHICLES C ONTEXT A scientific coordination group devoted to intelligent transportation systems (ITSs), called Groupement d’Intérêt Scientifique (GIS) ITS Bretagne, has been set up in the Brittany region of France to investigate this research area One of its projects, i.e., CAPTIV, aims at using existing infrastructure, i.e., not only road signs but also every infrastructure along the road, to transmit information inside a wireless network, including equipped vehicles, as illustrated in Fig The first applications offered by CAPTIV are road signs anticipated displays (including dynamic situations as temporary works on the The traditional model for the relay diversity technique with one relay node, as shown in Fig 2, consists of a source node S, a destination node D, and a relay node R The relay transmission from S to D can be performed by a two-time slot transmission In the first time slot, signals are transmitted by the source S to the destination node D and the relay node R at the same time In the second time slot, the relay node retransmits the information previously received At node D, the receiver combines received signals by using a diversity combination technique, e.g., maximum-ratio combination (MRC) or equalgain combination (EGC), before symbol detection In relay cooperative networks, the received signal comes from different independent fading channels so that the NGUYEN et al.: ENERGY-EFFICIENT COOPERATIVE TECHNIQUES FOR I2V COMMUNICATIONS 661 Fig Cooperative MIMO transmission scheme from S to D with N cooperative transmission nodes (S, CT,1 , CT,2 , , CT,N −1 ) and M cooperative reception nodes (D, CR,1 , CR,2 , , CR,M −1 ) probability of deep fading is minimized This diversity gain helps decrease the error rate or the transmission power for the same required error rate Relay techniques can be classified according to their forwarding strategy There are three main methods for the relay node to transmit the received frame to the destination node: 1) amplify and forward; 2) decode and forward; and 3) re-encode and forward The MIMO technique can exploit the diversity gain of the space–time coding technique to increase the system performance or to reduce the transmission consumption for the same bit-error-rate (BER) requirement The principle of cooperative MIMO transmission using STBCs was presented in [8] As illustrated in Fig 3, the cooperative MIMO transmission (with N cooperative transmissions and M cooperative reception nodes) from source node S to destination node D over a transmission distance d is composed of the following three phases: 1) local data exchange; 2) cooperative MIMO transmission; and 3) cooperative reception In the local data exchange at the transmission side, the source node S must cooperate with its neighbors and exchange its data to perform a MIMO transmission in the next phase Node S can broadcast the transmission bits to the other N − cooperative transmission nodes The distance between cooperating nodes dm is usually much smaller than the transmission distance d In the cooperative MIMO transmission phase, after N − neighbor nodes have received the data from source node S, N cooperative transmission nodes will modulate and encode their received bits to the quaternary phase-shift keying (QPSK) STBC symbols and then simultaneously transmit to the destination node (or multidestination nodes) similar to traditional MIMO systems (each cooperative node plays the role of one antenna of the MIMO system) Finally, in the cooperative reception phase at the reception side, cooperative neighbor nodes of destination node D receive the MIMO modulated symbols and then sequentially retransmit them to destination node D for joint MIMO signal combination and data decoding In a cooperative MIMO system, the decoder at destination node D requires the analog value of received signals at all cooperative nodes for the space–time combination Therefore, each cooperative node must transmit its received value through a wireless channel to destination node D One of the following three cooperative reception techniques can be used for this retransmission procedure: 1) quantization; 2) combine and forward; or 3) forward and combine [13] Fig FER of the relay technique versus the cooperative MISO technique with two transmission nodes, noncoded QPSK modulation over a Rayleigh channel, 120 b/frame, source–relay distance d1 = d/3, and power path loss factor K = B Performance Comparison of Cooperative Techniques Because the cooperative relay and cooperative MIMO technique can exploit the diversity gain to increase the performance, the performance of both techniques is much better than the SISO technique, and the signal-to-noise ratio (SNR) needed is smaller for the same BER requirement Fig represents the frame-error-rate (FER) performance comparison of the relay (decode-and-forward and amplify-and-forward techniques) and the cooperative MISO techniques for two transmit nodes with the traditional SISO technique Because the SNRs of the cooperative MISO and relay techniques are smaller than the SISO technique, the two cooperative techniques can help reduce the transmission energy consumption for the same transmission reliability in an energyconstrained traffic-signs wireless network This energy efficiency of cooperative MIMO and relay techniques is very useful for a typical medium- to long-distance transmission in ITS application, where the transmission energy consumption dominates the total consumption of a wireless node The nature of STBCs [14], [15] considers that signals from different transmit antennas must synchronously be received at each cooperative node to perform the orthogonal combination Furthermore, the clock of each wireless node can be drifted during transmission times, and the transmission delay can vary for each MIMO channel Consequently, it is impossible to have a perfectly synchronized transmission in distributed wireless nodes, leading to an unsynchronized received signal at the reception node The effect of the transmission synchronization error is the superposition of the signal pulses from each node, shifted by the corresponding time delay, at the receiver After the synchronization and the signal sampling, intersymbol interference (ISI) between the unsynchronized sequences appears, and the space–time sequences from the different nodes are no longer orthogonal The orthogonal combination of STBCs cannot be performed, which leads to the amplitude decrease of the desired signal and generates more interferences in the final estimated symbols [16] The effect of transmission synchronization in the performance of the cooperative MIMO technique for the case of 662 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL 12, NO 3, SEPTEMBER 2011 Fig Multihop SISO transmission between the infrastructure and a vehicle Fig Relay transmission between the infrastructure and a vehicle Fig Effect of the transmission synchronization error on the performance of the cooperative MISO systems with two transmit nodes N = and Alamouti STBC over a Rayleigh fading channel two transmit nodes is presented in Fig The performance degradation increases with the transmission synchronization error range The cooperative MIMO system is rather tolerant for a small range of transmission synchronization errors, and the degradation is negligible for a synchronization error range as small as 0.25Ts (and small for an error range as small as 0.5Ts ) For a small transmission synchronization error range, the performance degradation is small enough to keep the energy efficiency advantage of the cooperative MIMO system over the SISO and multihop SISO techniques However, the performance degradation is significant for transmission synchronization errors as large as 0.75Ts In this case, a more complex distributed STBC or an efficient space–time combination technique can be used to retain the performance of cooperative MIMO in the presence of a transmission synchronization error C Cooperative Transmission Schemes in the CAPTIV Project In several communication scenarios in ITS, the transmission between the infrastructure and the vehicles is usually from a medium to long distance, and a direct transmission, if possible, would need too much transmission energy A traditional multihop routing technique can be used for such transmissions, but it is not efficient enough in terms of energy consumption in several cases By exploiting the diversity transmission to reduce the transmission energy consumption, the relay and cooperative MIMO techniques are the better strategies in terms of energy efficiency Considering that the circle and the rectangle stand, respectively, for the road sign and the vehicle in the transport system, some cooperative transmission strategies, as illustrated in the following figures, have been proposed for energy efficiency transmissions in CAPTIV 1) SISO Multihop Transmission: The most simple cooperation scheme is the multihop SISO transmission, as shown in Fig Instead of the transmission over a long distance from source node S to destination node D, a message from a road sign (source node S) at a junction can be transmitted through multiple road signs (cooperation nodes) to a vehicle (destination node D) Multihop transmission can significantly Fig Cooperative MISO transmission between the infrastructure and a vehicle save the transmission energy consumption with the cost of more circuit energy consumption 2) Relay Transmission: In Fig 7, a message from the road sign can be transmitted to the vehicle (destination node D) and another road sign (relay node R) Then, the message is relayed from this relay road sign to the vehicle for signal combination The transmission diversity gain of the relay technique helps decrease the transmission power for the same error rate requirement so that it reduces the transmission energy consumption This technique is more energy efficient than multihop SISO for medium-range transmissions 3) Cooperative MIMO Transmission: The cooperative MIMO technique is an energy-efficient cooperative technique for medium- and long-range transmissions [9] The cooperative MIMO technique exploits the diversity gain of the MIMO space–time coding technique in distributed wireless networks to reduce the transmission energy consumption Depending on the system topology (the available nodes) and the transmission distance, the optimal selection of transmit and receive nodes number can be chosen to minimize the total energy consumption As illustrated in Fig 8, a road sign node S can cooperate with its neighbor road signs to employ a cooperative MISO NGUYEN et al.: ENERGY-EFFICIENT COOPERATIVE TECHNIQUES FOR I2V COMMUNICATIONS 663 Fig Cooperative MIMO transmission between the infrastructure and a vehicle Fig 12 Transmitter and receiver blocks with N transmit and M receive antennas TABLE I SNR R EQUIREMENT OF THE C OOPERATIVE MIMO T ECHNIQUE FOR −3 FER = 10 R EQUIREMENT AND A R AYLEIGH FADING C HANNEL Fig 10 Cooperative MIMO transmission between one infrastructure and another infrastructure (and cooperate together) to perform a multihop cooperative MIMO transmission III E NERGY E FFICIENCY OF C OOPERATIVE S TRATEGIES A Energy Consumption Model Fig 11 Multihop cooperative MIMO transmission between the infrastructure and a vehicle technique to transmit a message to the vehicle (destination node D) As shown in Fig 9, the road sign node S and the vehicle node D can cooperate with their respective neighbor road signs to employ a cooperative MIMO transmission over a long distance Because the vehicles not have the surface and energy consumption constraints, multiple antennas can easily be integrated in a vehicle to deploy the cooperative MIMO schemes without the need of the cooperative reception phase [9] Another example of cooperative MIMO transmission in CAPTIV is shown in Fig 10, where the road sign node S can cooperate with other road signs in one junction to transmit the message by using a cooperative MIMO technique to the cooperative reception road signs in the other junction 4) Multihop Cooperative MIMO Transmission: For a longdistance communication, the cooperative MIMO technique with the number of transmit and receive nodes greater than has energy consumption advantages [9], but this scenario cannot always be employed because of the lack of available nodes at the junctions In this condition, a multihop technique using cooperative MIMO for each transmission hop is a suitable solution As an example, for a communication between two crossroads with a distance greater than km in Fig 11, two road signs in the middle of the transmission line can be employed For a traditional MIMO system (noncooperative MIMO system) with N transmit and M receive antennas (N transmit antennas and M receive antennas are integrated into one transmitter and one receiver), the typical radio frequency (RF) system block of transmitters and receivers is shown in Fig 12 The total power consumption of a typical MIMO system consists of the following two components: 1) the transmission power Ppa of the power amplifier and 2) the circuit power Pc of all RF circuit blocks Ppa depends on the output transmission power Pout If the channel is a square-law path loss (power loss factor K = 2), the transmission power needed can be calculated as Pout (d) = E¯b Rb × (4πd)2 M l Nf Gt Gr λ2 (1) where E¯b is the required mean energy per bit for ensuring a given error rate requirement, Rb is the bit rate, and d is the transmission distance Gt and Gr are the transmission and reception antenna gains, respectively, λ is the carrier wave length, Ml is the link margin, and Nf is the noise figure receiver, which is defined as Nf = Mn /N0 , where N0 is the single-side thermal noise power spectral density (PSD), and Mn is the PSD of the total effective noise at receiver input Depending on the number of transmit and receive antennas (N and M ) and the PSD of thermal noise N0 , E¯b can be calculated based on the SN R value as given in Table I for the FER requirement FER = 10−3 and the performance result in Fig The power consumption Ppa can be approximated as Ppa = (1 + α)Pout (2) 664 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL 12, NO 3, SEPTEMBER 2011 where α = (ξ/η) − 1, with η being the drain efficiency of the RF power amplifier and ξ being the peak-to-average ratio (PAR), which depends on the modulation scheme and the associated constellation size Indeed, the power consumption of the amplifier is always higher than the effective output power The total circuit power consumption of N transmit and M receive antennas is given by TABLE II S YSTEM PARAMETERS FOR THE E NERGY C ONSUMPTION E VALUATION Pc ≈ N (PDAC + Pmix + Pf ilt + Psyn ) + M (PLN A + Pmix + PIF A + Pf ilr + PADC + Psyn ) (3) where PDAC , Pmix , PLN A , PIF A , Pf ilt , Pf ilr , PADC , and Psyn stand, respectively, for the power consumption values of the digital-to-analog converter, the mixer, the low-noise amplifier, the intermediate-frequency amplifier, the active filter at the transmitter and the receiver, the analog-to-digital converter, and the frequency synthesizer The power consumption of signal processing blocks in the transmitter and the receiver is typically much smaller than the consumption of RF blocks It is considered omitted in this estimation for simplicity The energy consumption of the traditional MIMO system EMIMO can be obtained as EMIMO = (Ppa + Pc ) Nb Rb (4) The energy consumption of the SISO technique or one hop of the SISO technique is the case that N = M = The energy consumption of one transmission phase (from nodes S to R and from nodes R to D) of the relay technique can be calculated similar to the SISO technique case For a cooperative MIMO system with N transmit and M receive nodes, there are three communication phases: 1) the data exchange phase; 2) the MIMO transmission phase; and 3) the cooperative reception phase The energy consumption of the MIMO transmission phase can be calculated similar to the noncooperative MIMO case The total energy consumption must include the energy consumption of cooperative data exchanges and cooperative reception phases The extra cooperative energy consumption at the transmission EcoopTx and reception Ecoop Rx sides can be calculated based on the noncooperative energy consumption model [9] The total energy consumption of a cooperative MIMO system with N transmit and M receive nodes is Etotal = EcoopTx + EMIMO + EcoopRx (5) For the case of cooperative MISO transmission (M = 1), there are only two first-communication phases, which means that the energy consumption of the reception phase EcoopRx is zero B Energy Consumption Comparison For energy consumption estimation, evaluation, and comparison, the reference energy model in [17] with the system parameters in Table II is used in this paper More details on the energy consumption calculation using this reference model can be consulted in [9] Figs 6–11 represent the total energy Fig 13 Energy consumption of SISO versus the cooperative MISO technique with two transmission nodes, power path loss factor K = 2, FER = 10−3 , and Rayleigh fading channel consumption to transmit 107 b with the FER requirement FER = 10−3 from a source node S to a destination node D separated by a distance d (over a Rayleigh fading channel) The local distance between cooperative nodes in the cooperative MIMO techniques is dm = m, and the source–relay distance in the relay techniques is d1 = d/3 1) Multihop SISO Versus Cooperative MISO Techniques: The energy consumption comparison between multihop SISO and the cooperative MISO is presented in Fig 13 with the optimal hop distance dhop = 25 m At the transmission distance d = 100 m (four hops), the multihop technique can save 53% of the total energy consumption of the SISO system The multihop technique is more efficient than the SISO transmission However, the multihop SISO system is 69% less energy efficient than the cooperative 2–1 MISO system At distance d = 100 m, 85% energy is saved by using the 2–1 cooperative MISO strategy instead of SISO Note that the total energy consumption is the consumption of all nodes and not only one source node The total energy saving is 69% or 85% for the whole network by using cooperative techniques The transmission energy consumption (which is always greater than the reception energy consumption for long distance) is shared by all cooperative transmission nodes Moreover, because the multihop system needs four hops for signal transmission to the destination node, the transmission delay of the multihop technique is much more than the cooperative MISO technique, which typically costs two phases of transmission Because the performance gain increases with the number of cooperative transmission nodes in cooperative MIMO techniques, the cooperative MISO 3–1 or MISO 4–1 is more NGUYEN et al.: ENERGY-EFFICIENT COOPERATIVE TECHNIQUES FOR I2V COMMUNICATIONS Fig 14 Energy consumption of the cooperative MISO technique with two, three, and four transmission nodes, power path loss factor K = 2, FER = 10−3 , and Rayleigh fading channel efficient than the cooperative MISO 2–1 or MISO 3–1 at d = 180 m or d = 300 m, respectively, as shown in Fig 14 If all the RF parameters and the transmission distance are fixed, the transmission energy consumption depends on the required energy per bit Eb and the power path loss factor of the channel [as shown in (1)] If the FER required increases (less reliable transmission), the required SNR and transmission energy consumption will decrease, reducing the energy efficiency advantage of the cooperative MIMO over the SISO and multihop SISO techniques Otherwise, if the path loss factor K increases (e.g., in an urban environment), the transmission energy consumption quickly increases (as a power function of the path loss factor K) Because the cooperative MIMO technique efficiently helps reduce the transmission energy, the advantage of cooperation increases As far as the frequency band is concerned, if the frequency fc = 5.8 GHz (which was elected by the European Union for ITS applications and is used in the delicate short-range communication technology) is considered instead of the reference model frequency 2.5 GHz used in this paper, the transmission energy consumption increases by (5.8/2.5)K times, and the cooperative MIMO technique will probably be more efficient Because the nodes are physically separated in a cooperative MIMO system, their different respective clocks lead to desynchronized transmission and reception This condition generates ISI, decreases the desired signal amplitude at the receiver, and makes it more difficult to estimate the channel-state information (CSI) At the reception side, each cooperative node has to forward its received signal through the wireless channel to the destination node for signal combination, which leads to additional noise in the final received signal The effect of synchronization error at the transmission side and this additive noise at the cooperative reception side lead to some performance degradations of the cooperative MIMO system [13] The transmission energy needs to be increased for the same error rate requirement, which will lead to an increase in the transmission energy and the total energy consumption The energy consumption of the cooperative phase (which depends on the cooperative distance dm ) is much smaller than 665 Fig 15 Energy consumption of the cooperative MISO 2–1 with different cooperative transmission distances dm = 5, 10, and 20 m, FER = 10−3 requirement, and Rayleigh block-fading channel with power path loss factor K = Fig 16 Total energy consumption of the cooperative MIMO with different reception techniques versus the cooperative MISO, ∆Tsyn = 0.25Ts , FER = 10−3 requirement, and Rayleigh fading channel with power path loss factor K = the consumption of the MIMO transmission phase for a longdistance transmission (because d dm ) Therefore, the variation of the cooperative transmission distance dm slightly affects the total energy consumption of the cooperative MIMO system Fig 15 shows the energy consumption of the cooperative MISO systems with different cooperative transmission distances dm = 5, 10, and 20 m 2) Cooperative MIMO Versus Cooperative MISO Techniques: Fig 16 shows the energy consumption comparison between the cooperative MIMO system with two receive nodes and the cooperative MISO systems 3–1 and 4–1 Forward and combine, combine and forward √ cooperative reception (with the amplification factor Kc = 4) [13], and quantization reception are used in the cooperative reception phase of the cooperative MIMO technique, and the transmission synchronization error range is considered ∆Ts = 0.25Ts The energy consumption of the cooperative MIMO 2–2 using the forward-and-combine cooperative reception technique is always smaller than the cooperative MISO 4–1 consumption 666 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL 12, NO 3, SEPTEMBER 2011 Fig 17 Optimal N − M transmit and receive antennas set selection as a function of transmission distance, ∆Tsyn = 0.25Ts , FER = 10−3 requirement, and Rayleigh fading channel with power path loss factor K = Fig 19 Energy consumption of the cooperative MISO technique as a function of transmission synchronization error range, two transmission nodes, error rate FER = 10−3 requirement, and Rayleigh fading channel with the power pathloss factor K = Fig 18 Energy consumption of the relay technique versus the cooperative MIMO technique with two transmission nodes, FER = 10−3 , power path loss factor K = 2, and source–relay distance d1 = d/3 and smaller than the cooperative MISO 3–1 consumption for distances d > 130 m At d = 500 m, there is a 25% energy savings using the cooperative MIMO 2–2 technique instead of the cooperative MISO 4–1 technique For each range of transmission distance d, based on the energy calculation result, we can find the best N − M antenna selection strategy of the cooperative MIMO technique in terms of the energy consumption, as shown in Fig 17 Note that, given the transmission distance and other parameters such as the quality of service (e.g., FER and the propagation channel), the global energy consumption must be calculated for every possible N − M configuration of cooperative MIMO by the analytic formula to perform the selection 3) Cooperative MISO Versus Relay Techniques: The performance of the relay techniques is limited by the decoding (or signal processing) process at the relay nodes The error bit (or amplification noise) that occurs at the relay node cannot always be corrected at the destination node However, with the same diversity gain, the performance of relay is always lower than MISO space–time coding techniques Therefore, in many cases, the total energy consumption of the relay technique is higher than the cooperative MISO technique Fig 18 shows the energy consumption of the relay technique compared with the SISO and cooperative MISO 2–1 techniques However, in the presence of transmission errors, the performance of the cooperative MISO technique decreases, leading to the increase of transmission energy consumption The energy consumption of the cooperative MISO 2–1 as a function of the transmission synchronization error range is illustrated in Fig 19 For a small synchronization error range, the degradation is negligible, but it becomes significant for a large error range, leading to a more required transmission energy [13] and less energy efficiency, as illustrated in Fig 19 The advantage of the relay technique over the cooperative technique is that the relay is not affected by the unsynchronized transmission Fig 20 shows the energy consumption comparison of the cooperative 2–1 and relay techniques with the path loss factor K = 3, and the transmission synchronization error range ∆Tsyn is as large as 0.5Ts In this condition, the relay technique is clearly better than the cooperative MISO in terms of energy consumption In the case that the number of cooperative transmission nodes N is greater than two (e.g., three or four transmit nodes), the relay technique typically needs N transmission phases to transmit all signals from N − relay nodes to the destination node (if orthogonal frequency channels are not considered) However, the cooperative MISO technique typically needs two transmission phases (data exchange and MISO transmission phases) The transmission delay of the relay technique is longer than the cooperative MISO technique However, the complexity of the relay is less than the cooperative MISO IV C ONCLUSION Cooperative techniques can exploit the transmission diversity gain to increase the performance or reduce the transmission energy consumption of the system Some cooperative strategies, which are based on the multihop, cooperative relay, and cooperative MIMO techniques, have been proposed to deploy energy-efficient transmissions between the road infrastructures and vehicles in CAPTIV In this paper, it has been shown that the cooperative MISO and MIMO techniques are more energy efficient than the NGUYEN et al.: ENERGY-EFFICIENT COOPERATIVE TECHNIQUES FOR I2V COMMUNICATIONS Fig 20 Energy consumption of the relay technique versus the cooperative MISO technique with two transmission nodes N = 2, power path loss factor K = 3, FER = 10−2 , transmission synchronization error range ∆Tsyn = 0.5Ts , and source–relay distance d1 = d/3 SISO and traditional multihop SISO techniques for mediumand long-range transmissions An optimal cooperative MIMO scheme selection has also been presented to find the optimal N − M antenna configuration for a given transmission distance Cooperative relay techniques provide attractive benefits for wireless distributed systems when the temporal and spatial diversity can be exploited to reduce the transmission energy consumption Relay techniques are more efficient than the SISO technique but are still less efficient than the cooperative MISO techniques in terms of energy consumption The performance of the relay techniques is not as good as the cooperative MISO techniques for the same SNR However, the relay techniques are not affected by the unsynchronized transmission scheme 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constraints,” in Proc IEEE Int Conf Commun., Anchorage, AK, May 2003, pp 2805–2811 Tuan-Duc Nguyen received the M.Sc degree from Telecom ParisTech University, Paris, France, and the Ph.D degree from the University of Rennes 1, Rennes, France, in 2005 and 2009, respectively In 2009, he was a Postdoctoral Researcher in cooperative communications for wireless sensor networks with the Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA) Research Center, University of Rennes Since 2010, he has been a Lecturer and Researcher with the School of Electrical Engineering, Ho Chi Minh City International University, Vietnam National University, Ho Chi Minh City, Vietnam His research interests include cooperative communications, wireless sensor networks, and wireless ad hoc networks R EFERENCES [1] J Laneman and G Wornell, “Energy-efficient antenna sharing and relaying for wireless networks,” in Proc IEEE Wireless Commun Networking Conf., 2000, vol 1, pp 7–12 [2] A Sendonaris, E Erkip, and B Aazhang, “User cooperation diversity—Part I: System description,” IEEE Trans Commun., vol 51, no 11, pp 1927–1938, Nov 2003 [3] J Laneman, D Tse, and G Wornell, “Cooperative diversity in wireless networks: Efficient protocols and outage behavior,” IEEE Trans Inf Theory, vol 50, no 12, pp 3062–3080, Dec 2004 [4] H Ilhan, I Altunbas, and M Uysal, “Cooperative diversity for relayassisted intervehicular communication,” in Proc IEEE Veh Technol Conf., 2008, pp 605–609 [5] M Dohler, E Lefranc, and H Aghvami, “Space–Time block codes for virtual antenna arrays,” in Proc 13th IEEE Int Symp Personal, Indoor Mobile Radio Commun., 2002, vol 1, pp 414–417 [6] X Li, “Energy-efficient wireless sensor networks with transmission diversity,” Electron Lett., vol 39, no 24, pp 1753–1755, Nov 2003 [7] J Laneman and G Wornell, “Distributed space–time-coded protocols for exploiting cooperative diversity in wireless networks,” IEEE Trans Inf Theory, vol 49, no 10, pp 2415–2425, Oct 2003 Olivier Berder received the B.S., M.S., and Ph.D degrees in electrical engineering from the University of Bretagne Occidentale, Brest, France, in 1998, 1999, and 2002, respectively From 2002 to 2004, he was with the Laboratory for Electronics and Telecommunication Systems (LEST–UMR CNRS 6165), Brest From October 2004 to February 2005, he was with the Speech and Sound Technologies and Processes Laboratory, FT R&D, Lannion, Brittany, France In March 2005, he was with the École Nationale Supérieure des Sciences Appliquées et de Technologie (ENSSAT)–University of Rennes 1, Rennes, France He is currently an Assistant Professor with the Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), University of Rennes His research interests focus on multiantenna systems and cooperative techniques for mobile communications and wireless sensor networks 668 IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, VOL 12, NO 3, SEPTEMBER 2011 Olivier Sentieys (M’03) received the M.Sc and Ph.D degrees in electrical engineering (signal processing) from the University of Rennes 1, Rennes, France, in 1990 and 1993, respectively After completing his Habilitation thesis in 1999, he was with the Graduate School of Electronics Engineering of the École Nationale Supérieure des Sciences Appliquées et de Technologie (ENSSAT), University of Rennes, as a Full Professor in 2002 He currently leads the CAIRN Research Team with the Institut National de Recherche en Informatique et en Automatique (INRIA; French National Institute for Research in Computer Science and Control) and the Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), University of Rennes His research interests include finite arithmetic effects, low-power and reconfigurable system on chip, the design of wireless communication systems, and cooperation in mobile systems He is a member of the editorial board of the Journal of Low Power Electronics He is the author or a coauthor of more than 150 journal publications or peer-reviewed conference proceedings and is the holder of five patents Prof Sentieys is the President of the French Chapter of IEEE Circuits and Systems (CAS) Society and a member of the Association for Computing Machinery (ACM) He was a Publicity Cochair of the 2010 IEEE International Symposium on Circuits and Systems and has been on several conference program committees, including the IEEE International Symposium on Quality Electronic Design, the IEEE International Symposium on Design and Diagnostics of Electronic Circuits and Systems, the IEEE Vehicular Technology Conference, the International Conference on Design and Technology of Integrated Systems, the Conference on Design of Circuits and Integrated Systems, and the IEEE Northeast Workshop on Circuits and Systems ... fading channels so that the NGUYEN et al.: ENERGY-EFFICIENT COOPERATIVE TECHNIQUES FOR I2V COMMUNICATIONS 661 Fig Cooperative MIMO transmission scheme from S to D with N cooperative transmission... nodes at a junction has to give arriving vehicles short-term information for driving assistance and long-term information for traffic management It is shown that the cooperative MIMO and relay techniques. .. D for joint MIMO signal combination and data decoding In a cooperative MIMO system, the decoder at destination node D requires the analog value of received signals at all cooperative nodes for